Chapter 6 - Medium

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Questions and Answers

What is the main difference between AlphaGo and AlphaZero?

AlphaGo is applied to Chess, while AlphaZero is applied to Go
AlphaGo learned from human games, while AlphaZero learned from self-play (correct)
AlphaGo uses MCTS, while AlphaZero uses P-UCT
AlphaGo uses reinforcement learning while AlphaZero uses supervised learning

What does the UCT formula calculate?

The expected value of an action
The upper confidence bound of an action (correct)
The probability of an action leading to a win
The number of times an action is visited

What is the main difference between UCT and P-UCT?

P-UCT does not use prior probabilities from a neural network
P-UCT incorporates prior probabilities from a neural network (correct)
UCT is used for self-play, while P-UCT is used for human games
UCT is used for Chess, while P-UCT is used for Go

What is the function of the Backpropagation step in MCTS?

To update the values of the nodes (B) Signup and view all the answers

What is the main purpose of MCTS?

To find the optimal action in a game (A) Signup and view all the answers

What is the main function of the Expansion step in MCTS?

To add a new node to the search tree (A) Signup and view all the answers

How does AlphaGo Zero learn?

From self-play without human data (D) Signup and view all the answers

What is the primary goal of the backpropagation step in MCTS?

To update the values of all nodes on the path from the leaf to the root (A) Signup and view all the answers

How does UCT balance exploration and exploitation?

By using a formula that balances the average reward with the exploration term (D) Signup and view all the answers

What is the effect of a small Cp value on MCTS?

It tends to exploit more (A) Signup and view all the answers

What is the primary advantage of tabula rasa learning?

It avoids the constraints of biased data and explores the search space more freely (A) Signup and view all the answers

What is a key difference between a double-headed network and a regular actor-critic?

The number of outputs (B) Signup and view all the answers

What is the purpose of the self-play loop in MCTS?

To update the policy and train the neural network (B) Signup and view all the answers

What is the primary goal of simulation in MCTS?

To obtain an outcome from a new state (A) Signup and view all the answers

What is the primary purpose of the UCT policy in MCTS?

To guide the selection and expansion steps (A) Signup and view all the answers

What is the main goal of Curriculum Learning?

To improve the agent's performance by gradually increasing task difficulty (D) Signup and view all the answers

What is the main difference between UCT and P-UCT policies?

P-UCT incorporates prior probabilities from a neural network (D) Signup and view all the answers

What is the goal of the backpropagation step in MCTS?

To update the Q-values and N-values (D) Signup and view all the answers

What is Self-Play Curriculum Learning?

Gradually increasing the difficulty of self-play tasks to improve the agent's performance (B) Signup and view all the answers

What is the purpose of the exploration/exploitation trade-off in MCTS?

To balance the exploration of new actions with the exploitation of known rewarding actions (C) Signup and view all the answers

What is Procedural Content Generation?

Automatically generating tasks or environments to train the agent (B) Signup and view all the answers

What is AlphaGo Zero?

A program that learned to play Go from scratch using self-play (C) Signup and view all the answers

What is the output of the MCTS algorithm?

The arg max of Q(N0, a) (C) Signup and view all the answers

What is the purpose of the policy network in MCTS?

To approximate the policy (B) Signup and view all the answers

What is the General Game Architecture used in AlphaZero and similar programs?

A combination of neural networks with MCTS (B) Signup and view all the answers

What is the common application of MCTS?

Game playing, such as Go and Chess (D) Signup and view all the answers

What is the main goal of Active Learning?

To allow the agent to choose the most informative examples to learn from (A) Signup and view all the answers

What is the purpose of regularization in MCTS?

To ensure stable learning (A) Signup and view all the answers

What is Single-Agent Curriculum Learning?

Applying curriculum learning techniques in a single-agent context to improve performance (B) Signup and view all the answers

What is the Open Self-Play Frameworks?

Open frameworks and tools for developing self-play agents (C) Signup and view all the answers

What is the primary goal of curriculum learning?

To improve generalization and learning speed (B) Signup and view all the answers

What is the key difference between AlphaGo and AlphaGo Zero?

The use of supervised learning from human games (D) Signup and view all the answers

What is the estimated size of the state space in Go?

10^170 (A) Signup and view all the answers

What is the main goal of the UCT formula in MCTS?

To balance exploration and exploitation (A) Signup and view all the answers

What is the main advantage of using self-play in AlphaGo Zero?

It enables the agent to learn from its own mistakes (A) Signup and view all the answers

What is the main difference between AlphaGo and conventional Chess programs?

The architectural elements used (B) Signup and view all the answers

How does MCTS work?

By selecting nodes to explore based on a balance of exploration and exploitation (B) Signup and view all the answers

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Study Notes

Monte Carlo Tree Search (MCTS)

MCTS is a search algorithm that balances exploration and exploitation using random sampling of the search space
It consists of four steps: Selection, Expansion, Simulation, and Backpropagation
Selection: selects the optimal child node recursively until a leaf node is reached
Expansion: adds one or more child nodes to the leaf node if it is not terminal
Simulation: runs a simulation from the new nodes to obtain an outcome
Backpropagation: updates the values of all nodes on the path from the leaf to the root based on the simulation result

Upper Confidence bounds applied to Trees (UCT)

UCT is a policy used in MCTS to select actions
It balances the average reward (exploitation) with the exploration term that favors less-visited actions
Formula: UCT = Q(s, a) + c * sqrt(ln N(s) / N(s, a))
P-UCT is a variant of UCT that incorporates prior probabilities from a neural network

Self-Play

Self-play is a training method where an agent learns by playing against itself
It consists of three levels: move-level, example-level, and tournament-level self-play
Example-level self-play involves training a policy and value network using neural networks
Tournament-level self-play involves training the agent on a sequence of tasks of increasing difficulty

Curriculum Learning

Curriculum learning is a method where an agent learns tasks in a sequence of increasing difficulty
It helps in better generalization and faster learning
Algorithm: Initialize curriculum C with tasks of increasing difficulty, train agent on each task using self-play

AlphaGo and AlphaZero

AlphaGo used supervised learning from human games and reinforcement learning
AlphaGo Zero learned purely from self-play without human data
AlphaZero is a generalization of AlphaGo Zero that achieved superhuman performance in Chess, Shogi, and Go
AlphaZero uses a neural network and MCTS to learn from self-play

Other Concepts

Tabula rasa learning: learning from scratch without any prior knowledge or data
Double-headed network: a neural network with two output heads, one for policy and one for value
Minimax: a decision rule used for minimizing the possible loss for a worst-case scenario in zero-sum games

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